class_docstr: CLIP text encoder.
class_docstr_tok: ["CLIP","text","encoder","."]
code: def __call__(
      self,
      text: jnp.ndarray,
      mask: Optional[jnp.ndarray] = None,
      *,
      train: bool = False,
      debug: bool = False,
  ) -> jnp.ndarray:
      del mask  # CLIP model computes its own mask.
      config = clip_model.CONFIGS[self.config_name]
      # We leave the default eps because the LayerNorm is always computed in FP32.
      clip_text_encoder = clip_layers.TextEncoder(
          out_features=config["embed_dim"],
          vocab_size=config["vocab_size"],
          features=config["text_features"],
          num_layers=config["text_num_layers"],
          num_heads=config["text_num_heads"],
          # dtype=self.dtype,
          name="text",
      )
      x = clip_text_encoder(text)
      if self.normalize:
          eps = jnp.finfo(x.dtype).eps
          return x / (jnp.linalg.norm(x, axis=-1, keepdims=True) + eps)
      else:
          return x
  
file_path: scenic/scenic/projects/lang4video/model/clip.py
imports: from typing import Optional
  import flax
  from flax import linen
  from flax.core.frozen_dict import FrozenDict
  import jax
  import jax.numpy
  from scenic.projects.baselines.clip import layers
  from scenic.projects.baselines.clip import model
  from scenic.projects.lang4video.model.base_encoders import ImageEncoder
  from scenic.projects.lang4video.model.base_encoders import TextEncoder
label_desc: Use image node_id 1 for calling the ClipTextEncoder obj's underlying member method code with example usage: obj.__call__(text, mask) and returns: unknown, x